Amazon's Chronos is a time series model framework that leverages language model architecture and training on billions of tokenized time series observations to provide accurate zero-shot forecasts, often matching or exceeding purpose-built models. The framework exploits sequential similarities between language and Time Series models by scaling and quantizing the data, then using a classification approach to learn distributions. Chronos has been shown to be less accurate and slower than traditional statistical models in some cases, but its potential for improving forecasting accuracy with large-scale computational resources is still being explored. The model's performance depends on various factors, including the quality of the training data, the choice of hyperparameters, and the specific use case. While Chronos shows promise, it is not yet a replacement for traditional Time Series models, and further research is needed to improve its accuracy and efficiency.